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This study compares the performance of female majority-owned new ventures (FNV) vs. male majority-owned new ventures (MNV). It analyzes the differences in levels of variables such as education, the same industry work experience of owners, and other venture level attributes between FNVs and MNVs. More importantly, this study employs decomposition techniques to determine the individual contribution from the intergender difference of each attribute on the performance of the new venture. For example, the study finds that, on average, the owners of an MNV possessed 3.4 years more of the same industry work experience than their FNV counterparts. This difference in work experience accounted for 47% of the “explained” gap [1] in Net Profits between the FNVs and MNVs.

This paper utilizes the Kauffman Firm Survey, a longitudinal dataset of 4,928 new ventures started in the USA in 2004. It employs Blinder-Oaxaca and Fairlie decomposition techniques in conjunction with OLS and Logit regressions. Both methods provide point estimates of contributions to the performance gap due to the heterogeneity in each attribute across the groups (FNV and MNV). This approach has a significant advantage over OLS or mediation analysis, which can only provide a directional analysis of the contributions of differences in attributes to performance.

The paper finds no performance gap between MNVs and FNVs. It further investigates whether the heterogeneous characteristics of MNVs vs FNVs are related to different effects on survival and performance. It finds that characteristics such as owners’ work experience in the same industry, average hours worked by owners in the new venture, the technology level of the venture, and its incorporation status are related with a differential impact on new venture survival and performance.

All firms in the dataset belonged to a single cohort (2004) of new ventures started in the US. Future studies are encouraged to develop a dataset from multiple geographies and founding over several years so that the results may be more generalizable.

The paper provides crucial practical guidance to policymakers, investors, and entrepreneurs. In general, policies that enhance the work experience of women entrepreneurs and provide access to infrastructure such as daycares, which may allow them to work more hours, would probably improve the performance of FNVs.

The paper furthers the literature on women entrepreneurship by analyzing point estimates of differential contribution of disparate variables to performance. From a methodological perspective, the study reconciles the results between regression and decomposition analyses.

Building on recent advances in inverse probability weighted identification and estimation of counterfactual distributions, the authors examine the history of wage earnings for women and their potential wage distributions in the United States. These potentials are two counterfactuals, what if women received men’s market “rewards” for their own “skills,” and what if they received the women’s rewards but for men’s characteristics? Using the Current Population Survey data from 1976 to 2013, the authors analyze the entire counterfactual distributions to separate the “structure” and human capital “composition” effect. In contrast to Maasoumi and Wang (2019), the reference outcome in these decompositions is women’s observed earnings distribution, and inverse probability methods are employed, rather than the conditional quantile approaches. The authors provide decision theoretic measures of the distance between two distributions, to complement assessments based on mean, median, or particular quantiles. We assess uniform rankings of alternate distributions by tests of stochastic dominance in order to identify evaluations robust to subjective measures. Traditional moment-based measures severely underestimate the declining trend of the structure effect. Nevertheless, dominance rankings suggest that the structure (“discrimination”?) effect is bigger than human capital characteristics.

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.

The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.

The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.

First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.

The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.

This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

Self-employment is often viewed as a more desirable work arrangement than working as an employee for a firm. Women are pushed into self-employment due to organizational factors, such as a shrinking workforce or limited job opportunities, while being attracted to self-employment by the many psychological and social benefits (e.g., independence, flexibility, work-life balance, job satisfaction). Despite more women moving into self-employment, this type of employment still has different financial consequences for men and women. This article investigates whether a pay gap exists for self-employed women after controlling for industry, occupation, and hours worked and seeks to quantify the gender wage gap for the self-employed. A sample of 467 self-employed independent contractors in the United States was examined from the 2008 National Study of the Changing Workforce. The results indicate a large financial disparity between self-employed women and men. On average men earned $54,959 as compared to women who earned on average $28,554. Regardless of the parity in education, work experience, number of hours worked, or occupations, women earn less than men in self-employment. Findings suggest the existence of the glass cage‐a phenomenon whereby self-employed women earn significantly less than self-employed men with limited abilities to narrow the economic inequality.

This paper investigates the extent, the determinants and the change in the gender pay gap in Vietnam in the period 2010–2016 in order to provide suggestions for policy adjustment to narrow gender pay inequality more effectively.

This study employs the propensity score matching (PSM) method to examine inequality in pay between female and male earners sharing identical characteristics. The analysis is conducted for both the full sample and various characteristic-based subsamples. This procedure is conducted for 2010 and 2016 separately to discover the change in gap and inequality during this period.

The matching results based on the data sets taken from the Vietnam Household Living Standards Survey (VHLSS) 2010 and 2016 affirm that gender income inequality in Vietnam, though persisted, decreased significantly in 2016 compared to 2010, and was insignificant in many subsamples in 2016. In addition to the observable determinants including educational level, occupation, economic sector and industry, unobservable factors are proved to also play an important role in creating the gender pay gap in Vietnam.

The research findings suggest that policies aimed at mitigating gender pay inequality should take into account both observable characteristics and unobservable factors such as unobservable gender differences that affect wages and gender discrimination in pay.

This is the first study using a matching technique to investigate gender wage gap in Vietnam. With up-to-date data, longer research period and the superiority of the method used in dealing with sample selection bias, the results obtained are more robust, more detailed and reliable.

When we take the lens of race, ethnicity, gender, and class to the collected academic work on women business owners, what does it reveal? What do we really know? Are there differing definitions of success across segments of the women businessowner demographics? Do the challenges faced by African American women entrepreneurs differ from those confronting white female entrepreneurs? Do immigrant female women businessowners face more significant institutional barriers than their counterparts who have been U.S. citizens for at least two generations? Are there similar reasons for starting their businesses?

The study will inform relevant workplace gendered policies at the university and other academic institutions.

A cross -sectional study was conducted on all the Arabian Gulf University full-time employees during the COVID-19 pandemic, in June 2021. A questionnaire was sent to the employees requesting data that included sociodemographic data, living conditions, psychological status, social aspects, work productivity and satisfaction. The generalized anxiety disorder questionnaire (GAD-7) was used to screen for anxiety.

Gender disparity was noted in the increase of responsibilities at home and towards young children, and in stress and anxiety. Women’s reductions in peer (90.7%) and social (88%) interactions were higher than among males (81.8%, 73%, respectively). There was a narrow gender gap in work productivity and workload. More males reported that the university was concerned about its employees during COVID-19 and were satisfied with the measures taken. There was a gender disparity with respect to superiors periodically checking if the workload was manageable and discussing the mental health and well-being of the employees.

Future research directions may focus on how gender impacts the perception and response to crises in higher education in Arab countries.

This is one of the few studies in the region that examined the gendered psychosocial impact of COVID-19 and productivity in the workplace using a higher education institution as an example.